feat(api): Add options for supporting various embedding models (#1192)

We need to support:
- asymmetric embedding models (#934)
- truncation policies (#933)
- varying dimensional output (#932) 

## Test Plan

```bash
$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
   --inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$  pytest -s -v -k together test_embeddings.py \
   --inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
   --inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```

llama_stack/distribution/routers/routers.py

@@ -6,7 +6,11 @@
 
 from typing import Any, AsyncGenerator, Dict, List, Optional
 
-from llama_stack.apis.common.content_types import URL, InterleavedContent, InterleavedContentItem
+from llama_stack.apis.common.content_types import (
+    URL,
+    InterleavedContent,
+    InterleavedContentItem,
+)
 from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
 from llama_stack.apis.eval import (
     BenchmarkConfig,
@@ -17,11 +21,13 @@ from llama_stack.apis.eval import (
 )
 from llama_stack.apis.inference import (
     EmbeddingsResponse,
+    EmbeddingTaskType,
     Inference,
     LogProbConfig,
     Message,
     ResponseFormat,
     SamplingParams,
+    TextTruncation,
     ToolChoice,
     ToolConfig,
     ToolDefinition,
@@ -215,6 +221,9 @@ class InferenceRouter(Inference):
         self,
         model_id: str,
         contents: List[str] | List[InterleavedContentItem],
+        text_truncation: Optional[TextTruncation] = TextTruncation.none,
+        output_dimension: Optional[int] = None,
+        task_type: Optional[EmbeddingTaskType] = None,
     ) -> EmbeddingsResponse:
         model = await self.routing_table.get_model(model_id)
         if model is None:
@@ -224,6 +233,9 @@ class InferenceRouter(Inference):
         return await self.routing_table.get_provider_impl(model_id).embeddings(
             model_id=model_id,
             contents=contents,
+            text_truncation=text_truncation,
+            output_dimension=output_dimension,
+            task_type=task_type,
         )